Tuesday, 15 November 2016

This week we examine how executives can more fully grasp complex evidence/analysis affecting their outcomes - and how analytics professionals can better communicate these findings to executives. Better performance and more trust are the payoffs.

1. Show how A → B. Our new guide to Promoting Evidence-Based Insights explains how to engage stakeholders with a data value story. Shape content around four essential elements: Top-line, evidence-based, bite-size, and reusable. It's a suitable approach whether you're in marketing, R&D, analytics, or advocacy.

No knowledge salad. To avoid tl;dr or MEGO (My Eyes Glaze Over), be sure to emphasize insights that matter to stakeholders. Explicitly connect specific actions with important outcomes, identify your methods, and provide a simple visual - this establishes trust and crediblity. Be succint; you can drill down into detailed evidence later. The guide is free from Ugly Research.

2. Lack of analytics understanding → Lack of trust. Great stuff from KPMG: Building trust in analytics: Breaking the cycle of mistrust in D&A. "We believe that organizations must think about trusted analytics as a strategic way to bridge the gap between decision-makers, data scientists and customers, and deliver sustainable business results. In this study, we define four ‘anchors of trust’ which underpin trusted analytics. And we offer seven key recommendations to help executives improve trust throughout the D&A value chain.... It is not a one-time communication exercise or a compliance tick-box. It is a continuous endeavor that should span the D&A lifecycle from data through to insights and ultimately to generating value."

Analytics professionals aren't feeling the C-Suite love.Information Week laments the lack of transparency around analytics: When non-data professionals don't know or understand how it is performed, it leads to a lack of trust. But that doesn't mean the data analytics efforts themselves are not worthy of trust. It means that the non-data pros don't know enough about these efforts to trust them.

3. Execs understand advanced analytics → See how to improve businessMcKinsey has an interesting take on this. "Execs can't avoid understanding advanced analytics - can no longer just 'leave it to the experts' because they must understand the art of the possible for improving their business."

Analytics expertise is widespread in operational realms such as manufacturing and HR. Finance data science must be a priority for CFOs to secure a place at the planning table. Mary Driscoll explains that CFOs want analysts trained in finance data science. "To be blunt: When [line-of-business] decision makers are using advanced analytics to compare, say, new strategies for volume, pricing and packaging, finance looks silly talking only in terms of past accounting results."

4. Macroeconomics is a pseudoscience. NYU professor Paul Romer's The Trouble With Macroeconomics is a widely discussed, skeptical analysis of macroeconomics. The opening to his abstract is excellent, making a strong point right out of the gate. Great writing, great questioning of tradition. "For more than three decades, macroeconomics has gone backwards. The treatment of identification now is no more credible than in the early 1970s but escapes challenge because it is so much more opaque. Macroeconomic theorists dismiss mere facts by feigning an obtuse ignorance about such simple assertions as 'tight monetary policy can cause a recession.'" Other critics also seek transparency: Alan Jay Levinovitz writes in @aeonmag The new astrology: By fetishising mathematical models, economists turned economics into a highly paid pseudoscience.

5. Better health evidence to a wider audience.From the Evidence Live Manifesto: Improving the development, dissemination. and implementation of research evidence for better health.

"7. Evidence Communication.... 7.2 Better communication of research: High quality, important research that matters has to be understandable and informative to a wide audience. Yet , much of what is currently produced is not directed to a lay audience, is often poorly constructed and is underpinned by a lack of training and guidance in this area." Thanks to Daniel Barth-Jones (@dbarthjones).

Tuesday, 11 October 2016

1. When nudging fails, what else can be done? Bravo to @CassSunstein, co-author of the popular book Nudge, for a journal abstract that is understandable and clearly identifies recommended actions. This from his upcoming article Nudges that Fail:

"Why are some nudges ineffective, or at least less effective than choice architects hope and expect? Focusing primarily on default rules, this essay emphasizes two reasons. The first involves strong antecedent preferences on the part of choosers. The second involves successful “counternudges,” which persuade people to choose in a way that confounds the efforts of choice architects. Nudges might also be ineffective, and less effective than expected, for five other reasons. (1) Some nudges produce confusion on the part of the target audience. (2) Some nudges have only short-term effects. (3) Some nudges produce “reactance” (though this appears to be rare) (4) Some nudges are based on an inaccurate (though initially plausible) understanding on the part of choice architects of what kinds of choice architecture will move people in particular contexts. (5) Some nudges produce compensating behavior, resulting in no net effect. When a nudge turns out to be insufficiently effective, choice architects have three potential responses: (1) Do nothing; (2) nudge better (or different); and (3) fortify the effects of the nudge, perhaps through counter-counternudges, perhaps through incentives, mandates, or bans."

This work will appear in a promising new journal, behavioral science & policy, "an international, peer-reviewed journal that features short, accessible articles describing actionable policy applications of behavioral scientific research that serves the public interest. articles submitted to bsp undergo a dual-review process. leading scholars from specific disciplinary areas review articles to assess their scientific rigor; at the same time, experts in relevant policy areas evaluate them for relevance and feasibility of implementation.... bsp is a publication of the behavioral science & policy association and the brookings institution press."

2. There will be defensive baseball stats! Highly recommended: Bruce Schoenfeld's writeup about Statcast, and how it will support development of meaningful statistics for baseball fielding. Cool insight into the work done by insiders like Daren Willman (@darenw). Finally, it won't just be about the slash line.

4. CATO says Donald Trump is wrong. Conservative think tank @CatoInstitute shares evidence that immigrants don’t commit more crimes. "No matter how researchers slice the data, the numbers show that immigrants commit fewer crimes than native-born Americans.... What the anti-immigration crowd needs to understand is not only are immigrants less likely to commit crimes than native-born Americans, but they also protect us from crimes in several ways."

Bad presentation alert: The report, The Economic and Fiscal Consequences of Immigration, offers no summary visuals and buries its conclusions deep within dense chapters. Perhaps methodology is the problem, documenting the "evidence-based consensus of an authoring committee of experts". People need concise synthesis and actionable findings: What can policy makers do with this information?

Bad reporting alert: Perhaps unsatisfied with these findings, Julia Preston of the New York Times slipped her own claim into the coverage, saying the report "did not focus on American technology workers [true], many of whom have been displaced from their jobs in recent years by immigrants on temporary visas [unfounded claim]". Rather sloppy reporting, particularly when covering an extensive economic study of immigration impacts.

Key evidence: "Empirical research in recent decades suggests that findings remain by and large consistent with those in The New Americans (National Research Council, 1997) in that, when measured over a period of 10 years or more, the impact of immigration on the wages of natives overall is very small." [page 204]

Immigration also contributes to the nation’s economic growth.... Perhaps even more important than the contribution to labor supply is the infusion by high-skilled immigration of human capital that has boosted the nation’s capacity for innovation and technological change. The contribution of immigrants to human and physical capital formation, entrepreneurship, and innovation are essential to long-run sustained economic growth. [page 243]

Author:@theNASEM, the National Academies of Sciences, Engineering, and Medicine.

Relationship: immigration → sustains → economic growth

2. Improving vs. proving. On @A4UEvidence: "We often assume that generating evidence is a linear progression towards proving whether a service works. In reality the process is often two steps forward, one step back." Ugly Research supports the 'what works' concept, but wholeheartedly agrees that "The fact is that evidence rarely provides a clear-cut truth – that a service works or is cost-beneficial. Rather, evidence can support or challenge the beliefs that we, and others, have and it can point to ways in which a service might be improved."

3. Who should make sure policy is evidence-based and transparent?Bad PR alert? Is it government's responsibility to make policy transparent and balanced? If so, some are accusing the FDA of not holding up their end on drug and medical device policy. A recent 'close-held embargo' of an FDA announcement made NPR squirm. Scientific American says the deal was this: "NPR, along with a select group of media outlets, would get a briefing about an upcoming announcement by the U.S. Food and Drug Administration a day before anyone else. But in exchange for the scoop, NPR would have to abandon its reportorial independence. The FDA would dictate whom NPR's reporter could and couldn't interview.

"'My editors are uncomfortable with the condition that we cannot seek reaction,' NPR reporter Rob Stein wrote back to the government officials offering the deal. Stein asked for a little bit of leeway to do some independent reporting but was turned down flat. Take the deal or leave it."

Tuesday, 20 September 2016

"The driving force behind MDRC is a conviction that reliable evidence, well communicated, can make an important difference in social policy." -Gordon L. Berlin, President, MDRC

1. Slice of the week: Can behavioral science improve the delivery of child support programs? Yes. Understanding how people respond to communications has improved outcomes. State programs shifted from heavy packets of detailed requirements to simple emails and postcard reminders. (Really, did this require behavioral science? Not to discount the excellent work by @CABS_MDRC, but it seems pretty obvious. Still, a promising outcome.)

2. How to test your decision-making instincts. McKinsey's Andrew Campbell and Jo Whitehead have studied decision-making for execs. They suggest asking yourself these four questions to ensure you're drawing on appropriate experiences and emotions. "Leaders cannot prevent gut instinct from influencing their judgments. What they can do is identify situations where it is likely to be biased and then strengthen the decision process to reduce the resulting risk."

Familiarity test: Have we frequently experienced identical or similar situations? Feedback test: Did we get reliable feedback in past situations? Measured-emotions test: Are the emotions we have experienced in similar or related situations measured? Independence test: Are we likely to be influenced by any inappropriate personal interests or attachments?

Relationship: Test of instincts → reduces → decision bias

3. When is enough evidence enough? At what point should we agree on the evidence, stop evaluating, and move on? Determining this is particularly difficult where public health is concerned. Despite all the available findings, investigators continue to study the costs and benefits of statin drugs. A new Lancet review takes a comprehensive look and makes a strong case for this important drug class. "Large-scale evidence from randomised trials shows that statin therapy reduces the risk of major vascular events" and "claims that statins commonly cause adverse effects reflect a failure to recognise the limitations of other sources of evidence about the effects of treatment".

Tuesday, 13 September 2016

PepperSliceBoard of the Week: Dentists will slow down on antibiotics if you show them a chart of their prescribing numbers.

Antimicrobial resistance is a serious public health concern. PLOS Medicine has published findings from an RCT studying whether quantitative feedback and intervention about prescribing patterns will reduce dentists' antibiotic RXs. An intervention group prescribed substantially fewer antibiotics per 100 cases.

This study evaluated the impact of providing general-practice dentists with individualised feedback consisting of a line graph of their monthly antibiotic prescribing rate. Rates in the intervention group were substantially lower than in the control group.

From the authors: "The feedback provided in this study is a relatively straightforward, low-cost public health and patient safety intervention that could potentially help the entire healthcare profession address the increasing challenge of antimicrobial resistance." Authors: Paula Elouafkaoui et al.

#: evidentista, antibiotics, evidence-based practice

2. Visualizing data distributions. Nathan Yau's fantastic blog, Flowing Data, offers a simple explanation of distributions - the spread of a dataset - and how to compare them. Highly recommended. "Single data points from a large dataset can make it more relatable, but those individual numbers don’t mean much without something to compare to. That’s where distributions come in."

3. Calculating 'expected value' of health interventions.Frank David provides a useful reminder of the realities of computing 'expected value'. Sooner or later, we must make simplifying assumptions, and compare costs and benefits on similar terms (usually $). On Forbes he walks us through a straightforward calculation of the value of an Epi-pen. (Frank's firm, Pharmagellan, is coming out with a book on biotech financial modeling, and we look forward to that.)

4. What is Bayesian, really? In the Three Faces of Bayes, @burrsettles beautifully describes three uses of the term Bayesian, and wonders "Why is it that Bayesian networks, for example, aren’t considered… y’know… Bayesian?" Recommended for readers wanting to know more about these algorithms for machine learning and decision analysis.

September 19-21; Boston. FierceBiotech Drug Development Forum. Evaluate challenges, trends, and innovation in drug discovery and R&D. Covering the entire drug development process, from basic research through clinical trials.

Tuesday, 30 August 2016

Dear reader: Evidence Soup is starting a new chapter. Our spotlight topics are now accompanied by a 'newsletter' version of a PepperSlice, the capsule form of evidence-based analysis we've created at PepperSlice.com. Let me know what you think, and thanks for your continued readership. - Tracy Altman

1. Is social services spending associated with better health outcomes? Yes. Evidence has revealed a significant association between healthcare outcomes and the ratio of social service to healthcare spending in various OECD countries. Now a new study, published in Health Affairs, finds this same pattern within the US. The health differences were substantial. For instance, a 20% change in the median social-to-health spending ratio was associated with 85,000 fewer adults with obesity and more than 950,000 adults with mental illness. Elizabeth Bradley and Lauren Taylor explain on the RWJF Culture of Health blog.

This is great, but we wonder: Where/what is the cause-effect relationship?

From the authors: "Reorienting attention and resources from the health care sector to upstream social factors is critical, but it’s also an uphill battle. More research is needed to characterize how the health effects of social determinants like education and poverty act over longer time horizons. Stakeholders need to use information about data on health—not just health care—to make resource allocation decisions."

2. Are nonfinancial metrics good leading indicators of financial performance? Maybe. During the '90s and early 00's we heard a lot about Kaplan and Norton's Balanced Scorecard. A key concept was the use of nonfinancial management metrics such as customer satisfaction, employee engagement, and openness to innovation. This was thought to encourage actions that increased a company’s long-term value, rather than maximizing short-term financials.

The idea has taken hold, and nonfinancial metrics are often used in designing performance management systems and executive compensation plans. But not everyone is a fan: Some argue this can actually be harmful; for instance, execs might prioritize customer sat over other performance areas. Recent findings in the MIT Sloan Management Review look at whether these metrics truly are leading indicators of financial performance.

From the authors: "We found that there were notable variations in the lead indicator strength of customer satisfaction in a sample of companies drawn from different industries. For instance, for a chemical company in our sample, customer satisfaction’s lead indicator strength was negative; this finding is consistent with prior research suggesting that in many industries, the expense required to increase customer satisfaction can’t be justified. By contrast, for a telecommunications company we studied, customer satisfaction was a strong leading indicator; this finding is consistent with evidence showing that in many service industries, customer satisfaction reduces customer churn and price sensitivity. For a professional service firm in our sample, the lead indicator strength of customer satisfaction was weak; this is consistent with evidence showing that for such services, measures such as trust provide a clearer indication of the economic benefits than customer satisfaction.... Knowledge of whether a nonfinancial metric such as customer satisfaction is a strongly positive, weakly positive, or negative lead indicator of future financial performance can help companies avoid the pitfalls of using a nonfinancial metric to incentivize the wrong behavior."

September 19-21; Boston. FierceBiotech Drug Development Forum. Evaluate challenges, trends, and innovation in drug discovery and R&D. Covering the entire drug development process, from basic research through clinical trials.

Tuesday, 23 August 2016

1. Management research: Alchemy → Chemistry? McKinsey's Michael Birshan and Thomas Meakin set out to "take a data-driven look" at the strategic moves of newly appointed CEOs, and how those moves influenced company returns. The accompanying podcast (with transcript), CEO transitions: The science of success, says "A lot of the existing literature is quite qualitative, anecdotal, and we’ve been able to build a database of 599 CEO transitions and add a bunch of other sources to it and really try and mine that database hard for what we hope are new insights. We are really trying to move the conversation from alchemy to chemistry, if you like."

The research was first reported in How new CEOs can boost their odds of success. McKinsey's evidence says new CEOs make similar moves, with similar frequency, whether they're taking over a struggling company or a profitable one (see chart). For companies not performing well, Birshan says the data support his advice to be bold, and make multiple moves at once. Depending how you slice the numbers, both external and internal hires fared well in the CEO role (8).

Is this science? CEO performance was associated with the metric excess total returns to shareholders, "which is the performance of one company over or beneath the average performance of its industry peers over the same time period". Bottom line, can you attribute CEO activities directly to excess TRS? Organizational redesign was correlated with significant excess TRS (+1.9 percent) for well-performing companies. The authors say "We recognize that excess TRS CAGR does not prove a causal link; too many other variables, some beyond a CEO’s control, have an influence. But we do find the differences that emerged quite plausible." Hmm, correlation still does not equal causation.

Examine the evidence. The report's end notes answer some key questions: Can you observe or measure whether a CEO inspires the top team? Probably not (1). Where do you draw the line between a total re-org and a management change? They define 'management reshuffle' as 50+% turnover in first two years (5). But we have other questions: How were these data collected and analyzed? Some form of content analysis would likely be required to assign values to variables. How were the 599 CEOs chosen as the sample? Selection bias is a concern. Were some items self-reported? Interviews or survey results? Were findings validated by assigning a second team to check for internal reliability? External reliability?

2. ICER + pharma → Fingerpointing. There's a kerfuffle between pharma companies and the nonprofit ICER (@ICER_review). The Institute for Clinical and Economic Review publishes reports on drug valuation, and studies comparative efficacy. Biopharma Dive explains that "Drugmakers have argued ICER's reviews are driven by the interests of insurers, and fail to take the patient perspective into account." The National Pharmaceutical Council (@npcnow) takes issue with how ICER characterizes its funding sources.

ICER has been doing some damage control, responding to a list of 'myths' about its purpose and methods. Its rebuttal, Addressing the Myths About ICER and Value Assessment, examines criticisms such as "ICER only cares about the short-term cost to insurers, and uses an arbitrary budget cap to suggest low-ball prices." Also, ICER's economic models "use the Quality-Adjusted Life Year (QALY) which discriminates against those with serious conditions and the disabled, 'devaluing' their lives in a way that diminishes the importance of treatments to help them."

3. Immortal time bias → Overstated findings. You can't get a heart transplant after you're dead. The must-read Hilda Bastian writes on Statistically Funny about immortal time bias, a/k/a event-free time or competing risk bias. This happens when an analysis mishandles events whose occurrence precludes the outcome of interest - such as heart transplant outcomes. Numerous published studies, particularly those including Kaplan-Meier analyses, may suffer from this bias.

4. Climate change → Weird weather? This week the US is battling huge fires and disastrous floods: Climate change, right? Maybe. There's now a thing called event attribution science, where people apply probabilistic methods in an effort to determine whether an extreme weather resulted from climate change. The idea is to establish/predict adverse impacts.

Evidence & Insights Calendar:

September 20-22; Newark, New Jersey. Advanced Pharma Analytics. How to harness real-world evidence to optimize decision-making and improve patient-centric strategies.

Tuesday, 09 August 2016

1. Behavioral economics → Healthcare innovation. Jaan Sidorov (@DisMgtCareBlog) writes on the @Health_Affairs blog about roadblocks to healthcare innovation. Behavioral economics can help us truly understand resistance to change, including unconscious bias, so valuable improvements will gain more traction. Sidoro offers concise explanations of hyperbolic discounting, experience weighting, social utility, predictive value, and other relevant economic concepts. He also recommends specific tactics when presenting a technology-based innovation to the C-Suite.

2. Laptops → Foster teen success. Nobody should have to type their high school essays on their phone. A coalition including Silicon Valley leaders and public sector agencies will ensure all California foster teens can own a laptop computer. Foster Care Counts reports evidence that "providing laptop computers to transition age youth shows measurable improvement in self-esteem and academic performance". KQED's California Report ran a fine story.

For a year, researchers at USC's School of Social Work surveyed 730 foster youth who received laptops, finding that "not only do grades and class attendance improve, but self-esteem and life satisfaction increase, while depression drops precipitously."

3. Analytical meritocracy → Better NBA outcomes. The Innovation Enterprise Sports Channel explain how the NBA draft is becoming an analytical meritocracy. Predictive models help teams evaluate potential picks, including some they might have overlooked. Example: Andre Roberson, who played very little college ball, was drafted successfully by Oklahoma City based on analytics. It's tricky combining projections for active NBA teams with prospects who may never take the court. One decision aid is ESPN’s Draft Projection model, using Statistical Plus/Minus to predict how someone would perform through season five of a hypothetical NBA career. ESPN designates each player as a Superstar, Starter, Role Player, or Bust, to facilitate risk-reward assessments.

4. Celebrity culture → Clash with scientific evidence. Health law and policy professor Timothy Caulfield (@CaulfieldTim) examines the impact of celebrity culture on people's choices of diet and healthcare. His new book asks Is Gwyneth Paltrow Wrong About Everything?: How the Famous Sell Us Elixirs of Health, Beauty & Happiness. Caulfield cites many, many peer-reviewed sources of evidence.

Tuesday, 02 August 2016

1. Systematic review: Does business coaching make a difference? In PLOSOne, Grover and Furnham present findings of their systematic review of coaching impacts within organizations. They found glimmers of hope for positive results from coaching, but also spotted numerous holes in research designs and data quality.

Over the years, outcome measures have included job satisfaction, performance, self-awareness, anxiety, resilience, hope, autonomy, and goal attainment. Some have measured ROI, although this one seems particularly subjective. In terms of organizational impacts, researchers have measured transformational leadership and performance as rated by others. This systematic review included only professional coaches, whether internal or external to the organization. Thanks @Rob_Briner and @IOPractitioners.

All data is not necessarily good data. “We were consistently seeing a 13–20% misattribution rate on surveys due in large part to recall problems. Resultantly, you get this chaos in your data and have to wonder what you can trust.... Rather than just trying to mitigate memory bias, can we actually use it to our advantage to offset issues with our brands?”

The ethics of manipulating memory. “We can actually affect people’s nutrition and the types of foods they prefer eating.... But should we deliberately plant memories in the minds of people so they can live healthier or happier lives, or should we be banning the use of these techniques?”

Mitigating researchers' memory bias. “We’ve been talking about memory biases for respondents, but we, as researchers, are also very prone to memory biases.... There’s a huge opportunity in qual research to apply an impartial technique that can mitigate (researcher) biases too....[I]n the next few years, it’s going to be absolutely required that anytime you do something that is qualitative in nature that the analysis is not totally reliant on humans.”

3. Female VC --> No gender gap for startup funding. New evidence suggests female entrepreneurs should choose venture capital firms with female partners (SF Business Times). Michigan's Sahil Raina analyzed data to compare the gender gap in successful exits from VC financing between two sets of startups: those initially financed by VCs with only male general partners (GPs), and those initially financed by VCs that include female GPs. “I find a large performance gender gap among startups financed by VCs with only male GPs, but no such gap among startups financed by VCs that include female GPs.”

4. Sharing evidence about student outcomes. Results for America is launching an Evidence in Education Lab to help states, school districts, and individual schools build and use evidence of 'what works' to improve student outcomes. A handful of states and districts will work closely with RFA to tackle specific data challenges.

Background: The bipartisan Every Student Succeeds Act (ESSA) became law in December 2015. ESSA requires, allows, and encourages the use of evidence-based approaches that can help improve student outcomes. Results for America estimates that ESSA's evidence provisions could help shift more than $2B US of federal education funds in each of the next four years toward evidence-based, results-driven solutions.

Tuesday, 26 July 2016

1. Panning for gold in the evidence stream. Patrick Lester introduces his new SSIR article by saying "With evidence-based policy, we need to acknowledge that some evidence is more valid than others. Pretending all evidence is equal will only preserve the status quo." In Defining Evidence Down, the director of the Social Innovation Research Center responds to analysts skeptical of evidence hierarchies developed to steer funding toward programs that fit the "what works" concept.

Are levels valid? Hierarchies recognize different levels of evidence according to their rigor and certainty. These rankings are well-established in healthcare, and are becoming the standard for evidence evaluation within the Dept of Education and other US government agencies. Critics of this prevailing thinking (Gopal & Schorr, Friends of Evidence) want to ensure decision-makers embrace an inclusive definition of evidence that values qualitative research, case studies, insights from experience, and professional judgment. Lester contends that "Unfortunately, to reject evidence hierarchies is to promote is a form of evidence relativism, where everyone is entitled to his or her own views about what constitutes good evidence in his or her own local or individualized context.

Ideology vs. evidence. "By resisting the notion that some evidence is more valid than others, they are defining evidence down. Such relativism would risk a return to the past, where change has too often been driven by fads, ideology, and politics, and where entrenched interests have often preserved the status quo." Other highlights: "...supporting a broad definition of evidence is not the same thing as saying that all evidence is equally valid." And "...randomized evaluations are not the only rigorous way to examine systems-level change. Researchers can often use quasi-experimental evaluations to examine policy changes...."

2. Can innovation be systematic? Everyone wants innovation nowadays, but how do you make it happen? @HighVizAbility reviews a method called Systematic Inventive Thinking, an approach to creativity, innovation, and problem solving. The idea is to execute as a process, rather than relying on random ideas. Advocates say SIT doesn't replace unbridled creativity, but instead complements it.

3. Remembering decision analysis pioneer Howard Raiffa. Howard Raiffa, co-founder of the Harvard Kennedy School of Government and decision analysis pioneer, passed away recently. He was also a Bayesian decision theorist and well-known author on negotiation strategies. Raiffa considered negotiation analysis an opportunity for both sides to get value, describing it as The Science and Art of Collaborative Decision Making.

4. Journal impact factor redux? In the wake of news that Thomson Reuters sold its formula, Stat says changes may finally be coming to the "hated" journal impact factor. Ivan Oransky (@ivanoransky) and Adam Marcus (@armarcus) explain that some evidence suggests science articles don't receive the high number of citations supposedly predicted by the IF. The American Society of Microbiologists has announced that it will abandon the metric completely. Meanwhile, top editors from Nature — which in the past has taken pride in its IF — have coauthored a paper widely seen as critical of the factor.